{"id":"https://openalex.org/W7135220087","doi":"https://doi.org/10.48550/arxiv.2603.11229","title":"Trustworthy predictive distributions for rare events via diagnostic transport maps","display_name":"Trustworthy predictive distributions for rare events via diagnostic transport maps","publication_year":2026,"publication_date":"2026-03-11","ids":{"openalex":"https://openalex.org/W7135220087","doi":"https://doi.org/10.48550/arxiv.2603.11229"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.11229","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11229","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.11229","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129034493","display_name":"Elizabeth Cucuzzella","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Cucuzzella, Elizabeth","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041145702","display_name":"Rafael Izbicki","orcid":"https://orcid.org/0000-0003-0379-9690"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Izbicki, Rafael","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5009280266","display_name":"Ann B. Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Ann B.","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5129034493"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11483","display_name":"Tropical and Extratropical Cyclones Research","score":0.7562999725341797,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11483","display_name":"Tropical and Extratropical Cyclones Research","score":0.7562999725341797,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.1517000049352646,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13018","display_name":"Seismology and Earthquake Studies","score":0.00559999980032444,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.6705999970436096},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.5048999786376953},{"id":"https://openalex.org/keywords/posterior-predictive-distribution","display_name":"Posterior predictive distribution","score":0.5001000165939331},{"id":"https://openalex.org/keywords/trustworthiness","display_name":"Trustworthiness","score":0.41519999504089355},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.41280001401901245},{"id":"https://openalex.org/keywords/rare-events","display_name":"Rare events","score":0.4023999869823456},{"id":"https://openalex.org/keywords/scoring-rule","display_name":"Scoring rule","score":0.37070000171661377},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.37049999833106995}],"concepts":[{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.6705999970436096},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5842999815940857},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5228999853134155},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.5048999786376953},{"id":"https://openalex.org/C83247935","wikidata":"https://www.wikidata.org/wiki/Q7234227","display_name":"Posterior predictive distribution","level":5,"score":0.5001000165939331},{"id":"https://openalex.org/C153701036","wikidata":"https://www.wikidata.org/wiki/Q659974","display_name":"Trustworthiness","level":2,"score":0.41519999504089355},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.41280001401901245},{"id":"https://openalex.org/C2777317252","wikidata":"https://www.wikidata.org/wiki/Q18393516","display_name":"Rare events","level":2,"score":0.4023999869823456},{"id":"https://openalex.org/C63002673","wikidata":"https://www.wikidata.org/wiki/Q2260590","display_name":"Scoring rule","level":2,"score":0.37070000171661377},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.37049999833106995},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.3702000081539154},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.3668000102043152},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.36660000681877136},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.3587999939918518},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35659998655319214},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.3513999879360199},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.34279999136924744},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3294000029563904},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.29829999804496765},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.2937999963760376},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.28060001134872437},{"id":"https://openalex.org/C917703","wikidata":"https://www.wikidata.org/wiki/Q7239668","display_name":"Predictive inference","level":5,"score":0.2773999869823456},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.27250000834465027}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.11229","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11229","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.11229","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11229","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.5316014289855957}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Forecast":[0],"systems":[1,36],"in":[2,61,85],"science":[3],"and":[4,26,52,70,98,155,163,192,206],"technology":[5],"are":[6,79],"increasingly":[7],"moving":[8],"beyond":[9],"point":[10],"prediction":[11],"toward":[12],"methods":[13],"that":[14,118,149,194],"produce":[15],"full":[16,39],"predictive":[17,40,94,171,209],"distributions":[18],"of":[19,29,135,225],"future":[20],"outcomes":[21],"y,":[22],"conditional":[23,133],"on":[24],"high-dimensional":[25],"complex":[27],"sequences":[28],"inputs":[30],"x.":[31],"However,":[32],"even":[33],"when":[34],"forecast":[35],"provide":[37,142],"a":[38,71,102,169,174],"distribution,":[41],"the":[42,121,131,143,152,178,208,222,226],"result":[43],"is":[44],"rarely":[45],"calibrated":[46],"with":[47,145,177,203],"respect":[48],"to":[49,76,82,128,186,221],"all":[50],"x":[51],"y.":[53],"The":[54],"estimated":[55],"density":[56],"can":[57],"be":[58,126],"especially":[59],"unreliable":[60],"low-frequency":[62],"or":[63],"out-of-distribution":[64],"regimes,":[65],"where":[66,151],"accurate":[67],"uncertainty":[68],"quantification":[69],"means":[72],"for":[73,211],"human":[74],"experts":[75],"verify":[77],"results":[78],"most":[80],"needed":[81],"establish":[83],"trust":[84],"models.":[86],"In":[87],"this":[88],"paper,":[89],"we":[90],"take":[91],"an":[92,195],"initial":[93],"distribution":[95,134,172],"as":[96,101,219],"given":[97],"treat":[99],"it":[100,157],"useful":[103],"but":[104],"potentially":[105],"misspecified":[106],"base":[107,122,179],"model.":[108,180],"WE":[109],"then":[110],"introduce":[111],"diagnostic":[112,183],"transport":[113,184],"maps,":[114],"covariate-dependent":[115],"probability-to-probability":[116],"maps":[117,141,185],"quantify":[119],"how":[120,156],"model's":[123],"probabilities":[124],"should":[125],"adjusted":[127],"better":[129],"match":[130],"true":[132],"calibration":[136],"data.":[137],"At":[138],"deployment,":[139],"these":[140],"user":[144],"real-time":[146],"local":[147,204],"diagnostics":[148],"reveal":[150],"model":[153],"fails":[154,158],"(including":[159],"bias,":[160],"dispersion,":[161],"skewness,":[162],"tail":[164],"errors),":[165],"while":[166],"also":[167],"producing":[168],"recalibrated":[170],"through":[173],"simple":[175],"composition":[176],"We":[181],"apply":[182],"short-term":[187],"tropical":[188],"cyclone":[189],"intensity":[190,217],"forecasting":[191],"show":[193],"easy-to-fit":[196],"parametric":[197],"version":[198],"identifies":[199],"evolutionary":[200],"modes":[201],"associated":[202],"miscalibration":[205],"improves":[207],"performance":[210],"rare":[212],"events,":[213],"including":[214],"24-hour":[215],"rapid":[216],"change,":[218],"compared":[220],"operational":[223],"forecasts":[224],"National":[227],"Hurricane":[228],"Center.":[229]},"counts_by_year":[],"updated_date":"2026-03-14T06:46:50.379900","created_date":"2026-03-14T00:00:00"}